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Effectively Scale and Operate AEM with MongoDB
Agenda
MongoDB Introduction MongoMK Sizing Deployment Operational Best Practices
Introduction
Norberto Leite Technical Evangelist Madrid, Spain http://www.mongodb.com/norberto @nleite norberto@mongodb.com
introduction
MongoDB
GENERAL PURPOSE DOCUMENT DATABASE OPEN-SOURCE
MongoDB is Fully Featured
Query Operators
Condi&onal Operators $all, $exists, $mod, $ne, $in, $nin, $nor, $or, $size, $type $lt, $lte, $gt, $gte
// find customers with any claims > db.customers.find( {claims: {$exists: true }} ) // find customers matching a regular expression > db.customers.find( {last: /^rog*/i } ) // count customers by city > db.customers.find( {city: ‘Philadelphia’} ).count()
Indexes
// Index nested documents > db.customers.ensureIndex( “policies.agent”:1 ) > db.customers.find({‘policies.agent’:’Fred’})
// geospatial index > db.customers.ensureIndex( “property.location”: “2d” ) > db.customers.find( “property.location” : { $near : [22,42] } )
// text index > db.customers.ensureIndex( “policies.notes”: “text” )
Documents are Rich Data Structures { ! first_name: ‘Paul’,! surname: ‘Miller’,! cell: 447557505611, ! city: ‘London’,! location: [45.123,47.232],! Profession: [banking, finance, trader],! cars: [ ! { model: ‘Bentley’,! year: 1973,! value: 100000, … },! { model: ‘Rolls Royce’,! year: 1965,! value: 330000, … }! ]!}!
Fields can contain an array of sub-documents
Fields
Fields can contain arrays
Typed field values String
Number
Over 10,000,000 downloads
300,000 Students for MongoDB
University
35,000 attendees to
MongoDB events annually
Over 1,000 Partners
Over 2,000!Paying Customers
MongoMK
MongoDB MicroKernel
• AEM 6 Scalable Storage Solution • Flexible • Multi-Datacenter Deployments • Geo Distributed Content • Auto-Failover
MongoMK
Nodes Blobs
Se4ngs
Changes
ClusterNodes
Metadata collec&on Content node structure Internal AEM indexes MulL version structures
Blobs collec&on Binary file chunks Enforces data de-‐duplicaLon
Se4ngs, Changes, ClusterNodes Internal AEM collecLons ConfiguraLon and seRngs data
MongoMK - Content
Metadata Binary / Blobs
Binary / Blobs
MongoMK – Metadata
Metadata
MongoMK – Data Model
MongoMK – Version Control
Revisions of content are maintained as separate trees
MongoMK – Data Model
MongoMK – Versioning & Concurrency
System provides version and concurrency control of content revisions.
MongoMK – Versioning & Concurrency
Binary / Blobs
MongoMK – Binary Data
Metadata
AEM Blob Storage
TAR MONGODB S3
Blobs on MongoMK
Posi&ve Penalty
Unified Storage Layer More Storage Requirements
Reduced OperaLonal Overhead More I/O Intensive
Binary De-‐duplicaLon Higher Bandwidth Requirements
MongoMK - Blobs
MongoMK - Blobs
Sizing
MONGODB SIZING
Availability
Volume
Expected Latency Working Data Set
Availability
Sizing - Availability
AEM
Edi&ng
Cura&ng
Valida&ng
Primary
Secondary
Secondary
Sizing - Availability
AEM
Edi&ng
Cura&ng
Valida&ng
Primary
Secondary
Primary
Sizing - Availability
AEM -‐ author AEM -‐ author AEM -‐ author
Primary
Secondary Secondary
Datacenter West Datacenter East Datacenter Center
AEM -‐ author AEM -‐ author
Sizing - Availability
AEM -‐ author AEM -‐ author AEM -‐ author
Primary
Secondary Secondary
Backups (hidden) Hot Backups (delayed)
Volume
Sizing - Volume
Indexes ProperLes MulL Version
Nodes
Blobs
Full Text Search Indexes
Binary Chunks
Sizing - Volume
• Space required by – Data – Indexes
• Read / Write Ratio • Computational Unit Capacity
– RAM – Disk
• Types of Disks! – CPU
Working Set
Sizing – Working Set
Working Set
Rest of your Database
Sizing – Working Set
• Percentage of data that is constantly request by the application – Indexes – Recent Used Data
• Read / Write – Impacts the calculation of the RAM requirement
• Working Set Should Fit In RAM
Working Set Not in RAM
Sizing – Working Set
• AEM Calculation of Working Set – Internal MongoDB Indexes – Constantly accessed Assets
• % of data access – AEM Indexes
• Multi-version Indexes • Lucene Indexes
Working Set Can Be Distributed Across Shards
Sizing - Latency
Primary
Secondary
Secondary
AEM
AEM
AEM
ONLY SECONDARY READS!
Deploy
Rules for a Good Deployment
Prototype
Test
Monitor Scale
Automate
AEM + Ops Manager
Scale Easily Meet SLAs
Best PracLces, Automated
Cut Management Overhead
Demo
Operational Best Practices
Node Documents { "_id": "1:/norberto", "_deleted": { "r14e211fe903-0-1": "false" }, "jcr:created": { "r14e211fe903-0-1": "\"dat:2015-06-23T17:53:36.257+02:00\"" }, "jcr:createdBy": { "r14e211fe903-0-1": "\"admin\"" }, "_commitRoot": { "r14e211fe903-0-1": "0" }, "jcr:primaryType": { "r14e211fe903-0-1": "\"nam:nt:folder\"" }, "_modified": NumberLong("1435074815"), "_modCount": NumberLong("1") }
{ "_id": "1:/norberto", … "_commitRoot": { "r14e211fe903-0-1": "0", "r14e2122a4bb-0-1": "0" }, … "_modified": NumberLong("1435074995"), "_modCount": NumberLong("3"), "_children": true, "_lastRev": { "r0-0-1": "r14e2122a4bb-0-1" } }
Document Moves 2015-05-18T17:07:10.264+0200 [conn1298] command aem.$cmd command: findAndModify { findandmodify: "nodes", query: { _id: "10:/content/www/...", _modCount: 3 }, fields: { _id: 1 }, update: { $set: { ... }, $max: { _modified: 1431961630 }, $inc: { _modCount: 1 } } } update: { $set: { ... }, $max: { _modified: 1431961630 }, $inc: { _modCount: 1 } } nscanned:1 nscannedObjects:1 nmoved:1 nMatched:1 nModified:1 keyUpdates:0 numYields:0 locks(micros) w:116270 reslen:203 116ms!
nscanned:1464 nscannedObjects:1044 nmoved:288 nMatched:1044 nModified:1044 keyUpdates:1 numYields:11 locks(micros) w:242369 169ms!
nscanned:10829 nscannedObjects:10000 nmoved:1316 nMatched:10000 nModified:10000 keyUpdates:1 numYields:89 l!ocks(micros) w:2372208 1234ms!
Fragmentation
AEM will be constantly growing the documents generating document moves and subsequently
producing fragmentation
db.adminCommand( { repairDatabase: 1 } )!
h_p://docs.mongodb.org/manual/reference/command/repairDatabase/
Monitoring Performance Indicators
Memory, Background Flushing Avg and Opcounters: make sure you understand these
metrics!
h_ps://docs.mms.mongodb.com/
Working Set Measurement
h_ps://docs.mms.mongodb.com/
Page Faults and Memory aligned with Db Storage will give you and good indication of how well provision is your machine and if you need to
scale out
To Sum It Up
Takeway
AEM + MongoDB is a Scalable Solution Provisioning is Key Monitor, monitor, monitor Wish List is Being Attended We Want to Help You!
MongoMK Deployment for AEM h_ps://info-‐mongodb-‐com.s3.amazonaws.com/MongoDB_AEM6_ConsulLng_Datasheet.pdf
Upcoming Webinar
https://www.mongodb.com/webinar/aem-and-mongodb
Obrigado!
Norberto Leite Technical Evangelist norberto@mongodb.com @nleite
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